[R] [fixed] vectorized nested loop: apply a function that takes two rows

Date: Tue 23 Jan 2007 - 20:46:27 GMT

(Extremely sorry, disregard previous email as I hit send before pasting the latest version of the example; this one is smaller too) Dear R users,

I want to apply a function that takes two vectors as input to all pairs (combinations (nrow(X), 2))of matrix rows in a matrix. I know that ideally, one should avoid loops in R, but after reading the docs for do.call, apply, etc, I still don't know how to write the nested loop in a vectorized way.

Example data:
x = matrix(rnorm(100), 10, 10)
# this is actually a very large sparse matrix, but it doesn't matter for the
# example
library(Matrix)
x = as(x,"CsparseMatrix")

# cosine function

cosine = function (x, y){

```	if (is.vector(x) && is.vector(y)) {
return(crossprod(x, y)/sqrt(crossprod(x) * crossprod(y)))
} else {stop("cosine: argument mismatch. Two vectors needed as input.")}
```
}
```		if (is(x, "Matrix") ) {
cos 	= array(NA, c(ncol(x), ncol(x)))
for (i in 2:ncol(x)) {
for (j in 1:(i - 1)) {
cos[i, j] = cosine(x[, i], x[, j])
}
}
}

```

This solution seems inneficient. Is there an easy way of achieving this with a clever do.call + apply combination?

Also, I have noticed that getting a row from a Matrix object produces a normal array (i.e., it does not inherit Matrix class). However, selecting >1 rows, does produce a same-class matrix. If I convert with as() the output of selecting one row, am I losing performance? Is there any way to make the resulting vector be a 1-D Matrix object?
This solution seems inneficient. Is there an easy way of achieving this with a clever do.call + apply combination?

```--
-Jose

--
Research fellow, Psychology Dept.
Sussex University, Brighton, UK